How to guide agents in a network with limited control?
Adaptive Network Intervention for Complex Systems: A Hierarchical Graph Reinforcement Learning Approach
This paper introduces Hierarchical Graph Reinforcement Learning (HGRL) for managing complex multi-agent systems, specifically focusing on network interventions. It aims to guide agent behavior by strategically adding or removing connections between agents in a network. The system manager, using HGRL, learns effective intervention policies even with limited authority, overcoming the "curse of dimensionality" faced by traditional reinforcement learning in large networks.
For LLM-based multi-agent systems, HGRL offers a promising approach for governing agent interactions by dynamically adjusting communication pathways. The hierarchical structure makes it scalable for large numbers of agents, which is crucial for complex LLM applications. The focus on limited intervention authority also aligns with the practical limitations of controlling autonomous LLM agents, making it a potentially valuable tool for promoting desired system-level outcomes while respecting agent autonomy. The paper also highlights the impact of social learning (agents imitating each other) on system behavior and overall welfare, a critical consideration when designing LLM-based multi-agent interactions.